Workflow

Detailed software versions can be found under Rules.

Results

Workflow resultes
File Size Description Job properties
plot_jup_nb.html 295.1 kB
Job properties
Ruleplot_nb_2_html
plot_rmd_shell.html 1.5 MB
Job properties
Ruleplot_rmd_shell
plot_rmd_shell.png 962.9 kB
Job properties
Ruleplot_rmd_shell
plot_rmd_snakemake.html 1.5 MB
Job properties
Ruleplot_rmd_snakemake

Statistics

If the workflow has been executed in cluster/cloud, runtimes include the waiting time in the queue.

Configuration

Configuration files
File Code
config/config.yml 
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repo_version:
  "0.0.1"

dataset:
  "iris"

Rules

Workflow rules
Rule Jobs Output Singularity Conda environment Code
plot_rmd_snakemake 1
  • docs/html/plot_rmd_snakemake.html
 
source
plot_rmd_shell 1
  • docs/html/plot_rmd_shell.html
  • docs/png/plot_rmd_shell.png
 
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R -e 'wd = getwd();                 rmarkdown::render( "{input[0]}",                     output_file = normalizePath( paste0( wd,"/" ,"{output[0]}" ) ),                     params = list(input = "{input[1]}",                                    output = "{output[1]}" ),                     knit_root_dir = getwd(),                     envir = new.env() )'
plot_nb_2_html 1
  • docs/html/plot_jup_nb.html
 
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jupyter nbconvert --to html {input} --output ../../../{output}
load_dataset 1
  • data/out/feather/data.feather
 
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import seaborn as sns
import pandas as pd

params = snakemake.config

df = sns.load_dataset(params['dataset'])

df.to_feather(snakemake.output[0])

print('load finished')
plot_execute_nb_plot 1
  • src/nb/executed/plot.ipynb
 
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python -c 'import papermill as pm;             pm.execute_notebook(  "{input[0]}"                     , "{output[0]}"                     , parameters = dict( input = "{input[1]}") )'